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Background <p>Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction&#xa0;(GP) using multi-omics data remains challenging.</p> Methods <p>We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML). We next estimated and ranked variant scores for 43 economically important traits, and compared the prediction accuracy of the top and bottom sets using genomic best linear unbiased prediction (GBLUP) and BayesB model. In addition, we annotated the variants from GWAS with functional feature sets and performed enrichment analysis.</p> Results <p>We observed significant enrichments for 32 functional categories in 11 feature sets. The evolutionary related sets (conservation regions and selection signatures) contributed significantly to heritability (31.78-fold and 14.48-fold enrichment), while metabolomics and transcriptomics showed low heritability enrichments. We observed a significant increase in prediction accuracy using the top feature set variants compared to whole-genome sequencing (WGS) data. The prediction accuracy based on the top 10% variant set showed an average increase of 11.6% and 7.54% using BayesB and GBLUP across traits, respectively. Notably, the greatest increase of 31.52% was obtained for spleen weight (SW) using BayesB. Also, we found that the top 10% of variants show strong enrichment with weight related QTLs based on the Cattle QTL database.</p> Conclusions <p>Our findings suggest that integrating biological prior information from multiple layers can enhance our understanding of the genetic architecture underlying complex traits and further improve genomic prediction in beef cattle.</p>

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Integrating multi-layered biological priors to improve genomic prediction accuracy in beef cattle

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2024年12月31日

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  • Zhida Zhao,
  • Qunhao Niu,
  • Jiayuan Wu,
  • Tianyi Wu,
  • Xueyuan Xie,
  • Zezhao Wang,
  • Lupei Zhang,
  • Huijiang Gao,
  • Xue Gao,
  • Lingyang Xu,
  • Bo Zhu,
  • Junya Li

摘要

Background

Integrating multi-layered information can enhance the accuracy of genomic prediction for complex traits. However, the improvement and application of effective strategies for genomic prediction (GP) using multi-omics data remains challenging.

Methods

We generated 11 feature sets for sequencing variants from genomics, transcriptomics, metabolomics, and epigenetics data in beef cattle, then we assessed the contribution of functional variants using genomic restricted maximum likelihood (GREML). We next estimated and ranked variant scores for 43 economically important traits, and compared the prediction accuracy of the top and bottom sets using genomic best linear unbiased prediction (GBLUP) and BayesB model. In addition, we annotated the variants from GWAS with functional feature sets and performed enrichment analysis.

Results

We observed significant enrichments for 32 functional categories in 11 feature sets. The evolutionary related sets (conservation regions and selection signatures) contributed significantly to heritability (31.78-fold and 14.48-fold enrichment), while metabolomics and transcriptomics showed low heritability enrichments. We observed a significant increase in prediction accuracy using the top feature set variants compared to whole-genome sequencing (WGS) data. The prediction accuracy based on the top 10% variant set showed an average increase of 11.6% and 7.54% using BayesB and GBLUP across traits, respectively. Notably, the greatest increase of 31.52% was obtained for spleen weight (SW) using BayesB. Also, we found that the top 10% of variants show strong enrichment with weight related QTLs based on the Cattle QTL database.

Conclusions

Our findings suggest that integrating biological prior information from multiple layers can enhance our understanding of the genetic architecture underlying complex traits and further improve genomic prediction in beef cattle.